With the recent increased use of image/visual information, high-resolution images are desired at reasonable costs. In the context of the current application, the high-resolution image process is related to sampling or scanning an original image into two-dimensional digitized image data and reproducing the original image based upon the sampled image data. The original image is reproduced on an image-carrying medium such as paper as well as on a display monitor. Because the above described image reproduction process is routinely preformed by personal computers with printers, digital copiers, facsimile machines and digital cameras, the improved resolution at a reasonable cost is commercially significant.
In order to reproduce high resolution images, one way is to digitize the original images by high-resolution input devices so that high resolution image data is generated. Input devices such as digital cameras require high resolution charge-coupled devices (CCD) for high-resolution images, and the high-resolution CCD's are generally expensive. At the time of the current invention, since a CCD of 1,000,000 pixels is substantially more expensive than a CCD of 400,000 pixels, an alternative way that does not require an expensive component is desired.
One type of attempts includes the signal processing of sampled data such as described in "Super-Resolved Surface Reconstruction From Multiple Images," by Cheeseman et al., pp. 0-12, Technical Report FIA-93-02, NASA AMS Research Center (1993). According to this approach, an original signal or image is reproduced from a plurality of sampled images such as satellite pictures based upon the Bayesian probability theory using a likelihood function which defines the probability of the observed data given a parameterized model for the data generation. In other words, the likelihood of the entire image is assumed to be just the product of likelihoods of each pixel. Each sampled data set thus includes the original signal and the aliasing noise both of which depend upon a set of sampling conditions or parameters. For this reason, it is difficult to isolate the original signal from the aliasing noise.
In the field of digital processing or computer graphics, it is known that if an original image is band limited below a half of the highest sampling frequency which is defined as a Nyquist frequency, and it is possible to reproduce an original image from the sampled signals. In other words, a signal can be properly reconstructed from its sampled data if the original signal is sampled at a frequency that is greater than twice the highest frequency component in its spectrum. In addition, it is also known that based upon multiple data sets that are sampled at displacement positions, an original image is reproduced. However, the displacement positions are precisely determined in advance and require an additional precision placement device. These restrictions are undesirable for reproducing an original image based upon the low resolution data sets using a low cost sampling device.